A self-organizing cascade neural network for nonlinear system modeling

This paper proposes a self-organizing cascade neural network (SCNN) for nonlinear system modeling. An objective function based on orthogonal least squares (OLS) method is proposed to select the input units and hidden units. After the new hidden unit is added to the network, its input weight remains unchanged in the subsequent training process and the output weights are updated in an incremental way. A stop criterion based on test error is proposed to select the optimal network structure. Finally, the proposed SCNN is tested on two benchmark nonlinear systems and an actual problem. The experimental results show that the proposed algorithm is efficient.